top of page
ABV_DiversityInclusion_1044x588.jpg

CS 4120 - Natural Language Processing 


Instructor: Malihe Alikhani
Time: Wednesday 6-9 pm

​

Course description

This course introduces students to the foundations of natural language processing (NLP), large language models (LLMs), and their applications. Through a mix of lectures, assignments, and presentations, students will gain both theoretical grounding and practical experience with modern NLP systems, from word embeddings to cutting-edge LLMs and their ethical considerations.

 

Schedule & Modules
 

Foundations (Weeks 1–3)

  • Introduction to NLP and word embeddings

  • Machine translation

  • Parsing, discourse, and coreference resolution

Large Language Models (Weeks 4–6)

  • Introduction to LLMs, architectures, and pre-training

  • Post-training methods and reinforcement learning with human feedback (RLHF)

  • Midterm presentations

  • Prompting methods: chain-of-thought, zero-shot reasoning, and more

Applications (Weeks 7–10)

  • Dialogue systems

  • Multimodality and robotics

  • Sign language processing

  • Open application topics

Ethics & Responsible AI (Weeks 11–12)

  • Social and ethical considerations in NLP and AI systems

  • Case studies and critical discussions

Final Projects (Weeks 13–14)

  • Wrap up and student final presentations
     

Key Dates

  • Sept 3: Course begins

  • Oct 8: Midterm presentations

  • Nov 5, 12, 19: Homework assignments due

  • Dec 3: Final presentations

​

Final research project

Final projects should ideally be done by teams of two students. Projects done by one or three students are possible on rare occasions with prior approval of the instructor. Each student is responsible for finding a partner for their final project. Feel free to post a message on Slack about your interests in order to find a partner with similar interests. ​

Submit a one-page project proposal by September 16 that briefly covers the first 4 questions for research paper presentations (see above). I will provide feedback on these proposals, but they will not be graded. Students are encouraged to discuss project proposals with me in office hours before submitting them. Groups are required to give a 6-minute presentation about their progress on the final project in the 7th week of the class. During the last week of class, each team will be required to give a 20-minute presentation on the current state of their project, and lead a 15-minute discussion of their project. This will allow for two project presentations per class. Use the same format as the paired research paper presentations (see above), but you may have only preliminary actual results at that time to present. Submit the code to the GitHub repo for the class at midnight on December 1. Final project reports are due on Canvas at midnight on December 10. They should be formatted to meet the requirements of paper submission to the ACL conference as described here.

​

Final grade

The final grade will be computed as follows:​

HW: 30%

  • HW0: 0% (optional)

  • HW1: 10% 

  • HW2: 10% 

  • HW3: 15% 

Midterm presentation: 20%

In-class assignments: 10%

Final Project: 30%​​

Participation: 5%

​

Academic integrity
All assignment submissions must be the sole work of each individual student. Students may not read or copy another student's solutions or share their own solutions with other students. Students may not review solutions from students who have taken the course in previous years. Submissions that are substantively similar will be considered cheating by all students involved, and as such, students must be mindful not to post their code publicly. The use of books and online resources is allowed, but must be credited in submissions, and material may not be copied verbatim. Any use of electronics or other resources during an examination will be considered cheating.

If you have any doubts about whether a particular action may be construed as cheating, ask the instructor for clarification before you do it. The instructor will make the final determination of what is considered cheating.
Cheating in this course will result in a grade of F for the course and may be subject to further disciplinary action.

Using an open-source codebase is accepted, but you must explicitly cite the source, especially following the owner's guideline if it exists. For any writing involved in the project, plagiarism is strictly prohibited. If you are unclear whether your work will be considered as plagiarism, ask the instructor before submitting or presenting the work

​

Students with disabilities

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course. More info at https://drc.sites.northeastern.edu/.

​

Audio/video recordings

To ensure the free and open discussion of ideas, students may not record classroom lectures, discussions, and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student's own private use. 

 

Copyrighted materials

All material provided through this web site is subject to copyright. This applies to class/recitation notes, slides, assignments, solutions, project descriptions, etc. You are allowed (and expected!) to use all the provided material for personal use. However, you are strictly prohibited from sharing the material with others in general and from posting the material on the Web or other file sharing venues in particular.

bottom of page